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About 10,000 papers for “Spatial correlation”

Spatial Correlation

Shashi Shekhar, Hui Xiong, Xun Zhou · Encyclopedia of GIS · 2017

Cited by 0

Spatial correlation test.

Hua Wang (13530), Yuxin Zhu (3512447), Jinghao Wang (296093) et al. · Figshare · 2022

Cited by 0Open access

Spatial correlation analysis.

Zhanfei Ma (12506951), Wenli Luo (12368370), Jing Jiang (130103) et al. · Figshare · 2023

<p>Correlation coefficients of meteorological data and pollutant concentration data at each station.</p>

Cited by 0Open access

Spatial Correlation Robust . . .

Timothy G. Conley, Francesca Molinari · 2003

This note presents preliminary results from a Monte Carlo study concerning inference with spatially dependent data. We investigate the impact of location/distance measurement problems upon the accuracy of parametric and nonparametric estimators of asymptotic variances. We consider measurement errors in distances, and locations that are known only up to broad areas like zip codes, SMSAs, or counties.

Cited by 0

(3) Spatial Correlation

A. Monfort, Simulation-based Econometric Method, Course Outline · 2013

This course presumes that students have completed ECN 725 and ECN 726 or equivalent. The goal of this course is to acquaint students with techniques they may wish to apply in thesis research. 2. TEXTBOOKS

Cited by 0

Spatial sign correlation

Alexander Dürre, Daniel Vogel, Roland Fried · Journal of Multivariate Analysis · 2014

Cited by 19Open access

Spatial correlation regression.

Ming Xu (151206), Zhaoyang Lu (345450) · Figshare · 2025

<div> Food security is the lifeline of national security. It is not only an important cornerstone for world peace, stability, and development, but also the core driving force for promoting the green development of agriculture. This paper utilizes the empirical data of 30 provinces in China from 2010 to 2022 to explore how agricultural non-point source pollution (ANSP) affects food security (FS), and also discusses the moderating effect of environmental regulations (ER). Empirical research shows that the intensification of agricultural non-point source pollution will lead to a decline in the level of food security, especially in the western areas, grain-producing areas, and grain-balanced areas. Furthermore, environmental regulations have a positive moderating effect on the impact of agricultural non-point source pollution on food security. From the perspective of the spatial spillover effect, the inhibitory effect of agricultural non-point source pollution on food security has a spillover effect in space and will radiate to the development of food security in adjacent areas. The research suggests that the local government should enhance its attention and supervision over agricultural non-point source pollution, integrate agricultural technology with environmental supervision, optimize the allocation of agricultural resources, and attach importance to the protection of the agricultural ecological environment, so as to better ensure food security and achieve green agricultural development. </div>

Cited by 0Open access

Spatial correlation and demography.

Sébastien Oliveau, Christophe Z. Guilmoto · 2005

Demographers often use maps and regional tabulations to evidence the spatial dimension of population characteristics. While they display considerable ability to test the strength of statistical links between phenomena, they rarely attempt to use similar tools to investigate the nature and extent of spatial correlation between demographic variables. This paper examines this question by combining theoretical and practical issues in the measurement of spatial autocorrelation, using recent Indian demographic variables. After a brief summary of the basic tools commonly used to examine geographical differentials, the paper includes a review of recent spatial exploratory and geostatistical tools. The second part of the paper is devoted to the application of these tools to a set of district-level variables derived from the Indian 2001 census, representing the major population characteristics as well other non-demographic variables. The conclusion sums some of the benefits and difficulties associated with spatial analysis in demography.

Cited by 4

The spatial correlation test.

Yangjun Ren (17691912), Ze Tian (85583), Chao Liu (43092) · Figshare · 2023

&lt;div&gt;&lt;p&gt;Using the digital economy to empower urban economic green growth provides essential opportunities for China to achieve high-quality growth. This paper assesses the level of digital economy and green growth in Chinese cities, seeking to explore the mechanisms and effects of the digital economy on urban green growth in a unified framework. The results indicate that the digital economy can drive cities’ green growth. This conclusion still holds after a set of robustness tests. Meanwhile, the green value of the digital economy is fully released among the eastern cities, major urban agglomerations, and high-level cities. Further research shows that the digital economy can indirectly enhance urban green growth in the neighboring regions through spatial spillover effects. Moreover, labor resource mismatch, capital resource mismatch and green technology innovation are significant mediating mechanisms. The findings could guide policymakers on green growth in emerging economies from a digital economy perspective.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Spatial Correlation Robust Inference

Ulrich K. Müller, Mark W. Watson · Econometrica · 2022

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.

Cited by 31

Spatial Correlation Robust Inference

Ulrich K. M\"uller, Mark W. Watson · RePEc: Research Papers in Economics

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar `estimator plus and minus a standard error times a critical value' form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given `worst-case' spatial covariance model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in large samples whenever the spatial correlation is weak, i.e., with average pairwise correlations that vanish as the sample size gets large. We also provide results on correct coverage in a restricted but nonparametric class of strong spatial correlations, as well as on the efficiency of the method. In a design calibrated to match economic activity in U.S. states the method outperforms previous suggestions for spatially robust inference about the population mean.

Cited by 0

Spatial Correlation and Variograms.

McKenna, Sean Andrew; Roberts, Barry L · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2020

Abstract not provided.

Cited by 0Open access